Somaye Hashemifar

NETAL: a new graph-based method for global alignment of protein–protein interaction networks

MOTIVATION The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together. RESULTS We present a novel algorithm for the global alignment of protein-protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein-protein interaction networks. AVAILABILITY Binaries supported on linux are freely available for download at http://www.bioinf.cs.ipm.ir/software/netal. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

HubAlign: an accurate and efficient method for global alignment of protein–protein interaction networks

Motivation: High-throughput experimental techniques have produced a large amount of protein–protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency. Results: This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins. Availability: HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip Contact: jinboxu@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Excessive expression of miR-27 impairs Treg-mediated immunological tolerance

MicroRNAs (miRs) are tightly regulated in the immune system, and aberrant expression of miRs often results in hematopoietic malignancies and autoimmune diseases. Previously, it was suggested that elevated levels of miR-27 in T cells isolated from patients with multiple sclerosis facilitate disease progression by inhibiting Th2 immunity and promoting pathogenic Th1 responses. Here we have demonstrated that, although mice with T cell–specific overexpression of miR-27 harbor dysregulated Th1 responses and develop autoimmune pathology, these disease phenotypes are not driven by miR-27 in effector T cells in a cell-autonomous manner. Rather, dysregulation of Th1 responses and autoimmunity resulted from a perturbed Treg compartment. Excessive miR-27 expression in murine T cells severely impaired Treg differentiation. Moreover, Tregs with exaggerated miR-27–mediated gene regulation exhibited diminished homeostasis and suppressor function in vivo. Mechanistically, we determined that miR-27 represses several known as well as previously uncharacterized targets that play critical roles in controlling multiple aspects of Treg biology. Collectively, our data show that miR-27 functions as a key regulator in Treg development and function and suggest that proper regulation of miR-27 is pivotal to safeguarding Treg-mediated immunological tolerance.

ModuleAlign: module-based global alignment of protein–protein interaction networks

MOTIVATION As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency. RESULTS In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen-Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis. AVAILABILITY http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html CONTACT canzar@ttic.edu or j3xu.ttic.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Lynx: a knowledge base and an analytical workbench for integrative medicine

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Chemokine expression in the early response to injury in human airway epithelial cells

Basal airway epithelial cells (AEC) constitute stem/progenitor cells within the central airways and respond to mucosal injury in an ordered sequence of spreading, migration, proliferation, and differentiation to needed cell types. However, dynamic gene transcription in the early events after mucosal injury has not been studied in AEC. We examined gene expression using microarrays following mechanical injury (MI) in primary human AEC grown in submersion culture to generate basal cells and in the air-liquid interface to generate differentiated AEC (dAEC) that include goblet and ciliated cells. A select group of ~150 genes was in differential expression (DE) within 2–24 hr after MI, and enrichment analysis of these genes showed over-representation of functional categories related to inflammatory cytokines and chemokines. Network-based gene prioritization and network reconstruction using the PINTA heat kernel diffusion algorithm demonstrated highly connected networks that were richer in differentiated AEC compared to basal cells. Similar experiments done in basal AEC collected from asthmatic donor lungs demonstrated substantial changes in DE genes and functional categories related to inflammation compared to basal AEC from normal donors. In dAEC, similar but more modest differences were observed. We demonstrate that the AEC transcription signature after MI identifies genes and pathways that are important to the initiation and perpetuation of airway mucosal inflammation. Gene expression occurs quickly after injury and is more profound in differentiated AEC, and is altered in AEC from asthmatic airways. Our data suggest that the early response to injury is substantially different in asthmatic airways, particularly in basal airway epithelial cells.

Predicting protein–protein interactions through sequence-based deep learning

Motivation High‐throughput experimental techniques have produced a large amount of protein‐protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data. Results We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese‐like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high‐quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state‐of‐the‐art methods on several benchmarks in terms of area under precision‐recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine‐receptor binding affinities. Availability and implementation Predicting protein‐protein interactions through sequence‐based deep learning): https://github.com/hashemifar/DPPI/. Supplementary information Supplementary data are available at Bioinformatics online.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Joint inference of tissue-specific networks with a scale free topology

High-throughput experimental techniques have produced an enormous number of gene expression profiles for various tissues of the human body. Tissue-specificity is a key component in reflecting the potentially different roles of proteins in diverse cell lineages. One way of understanding the tissue specificity is by reconstructing the tissue-specific co-expression networks (CENs) to analyze the correlation between genes. A few methods have been developed for estimating CENs, but it still remains challenging in terms of both accuracy and efficiency. In this paper we propose a new method, JointNet, for predicting tissue-specific co-expression networks. JointNet is exploiting the observation that, functionally related tissues have similar expression patterns and thus, similar networks. It uses different node penalties for hubs and non-hub nodes to accurately estimate the scale-free networks. Our experimental results show that the resulting tissue-specific CENs are accurate and that our method outperforms the current state of the art.